{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-the-joint-representation-of","title":"Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction","arxiv_id":"1803.04837","date":"2018-03-13","proceeding":null,"authors":["Lu-chen Liu","Jianhao Shen","Ming Zhang","Zichang Wang","Jian Tang"],"abstract":"The availability of a large amount of electronic health records (EHR)\nprovides huge opportunities to improve health care service by mining these\ndata. One important application is clinical endpoint prediction, which aims to\npredict whether a disease, a symptom or an abnormal lab test will happen in the\nfuture according to patients' history records. This paper develops deep\nlearning techniques for clinical endpoint prediction, which are effective in\nmany practical applications. However, the problem is very challenging since\npatients' history records contain multiple heterogeneous temporal events such\nas lab tests, diagnosis, and drug administrations. The visiting patterns of\ndifferent types of events vary significantly, and there exist complex nonlinear\nrelationships between different events. In this paper, we propose a novel model\nfor learning the joint representation of heterogeneous temporal events. The\nmodel adds a new gate to control the visiting rates of different events which\neffectively models the irregular patterns of different events and their\nnonlinear correlations. Experiment results with real-world clinical data on the\ntasks of predicting death and abnormal lab tests prove the effectiveness of our\nproposed approach over competitive baselines.","url_abs":"http://arxiv.org/abs/1803.04837v4","url_pdf":"http://arxiv.org/pdf/1803.04837v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-the-joint-representation-of","repo_url":"https://github.com/pkusjh/HELSTM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}